Electrocardiographic Data Research Articles

Electrocardiography Denoising via Sparse Dictionary Learning from Small Datasets

Abstract Wearable electrocardiography monitors, e.g. embedded in textile shirts, offer new approaches in diagnosis but suffers upon limited computational capacities. Hence, we propose and evaluate a lightweight algorithm for electrocardiography denoising via sparse dictionary learning, targeting two types of noise: baseline wander and muscle artifacts. For each type of noise a dictionary is built using K-singular value decomposition. This iterative method alternates between finding a sparse representation for every training signal and then updating every atom of the dictionary on its own. A sparse representation is found using the orthogonal matching pursuit algorithm. The atoms are updated exploiting the properties of the singular value decomposition. For further sparse approximation, we use the basis pursuit denoising algorithm. Electrocardiography data stems from synthetically-generated signals as well as the freely-available Brno University of Technology ECG Quality Database. Noise is added to the signals using the MIT-BIH Noise Stress Database. Our results regarding baseline wander demonstrate that the algorithm outperforms the American Heart Association-recommended bandpass filter w.r.t. signal-to-noise ratio. Moreover, a small number of training data is sufficient for satisfying results which indicates the suitability of the method for wearable hardware with low memory and power specifications.

Hybrid Learning Approach for Automated Identification and Categorization of Cardiovascular Disorders

ardio Vascular Diseases (CVDs) pose an important global health challenge, contributing substantially to mortality rates worldwide. Electrocardiography (ECG) is a necessary diagnostic tool in the detection of CVDs. Manual analysis by medical experts, for ECG interpretation, is laborious and subject to interobserver variability. To overcome these limitations, automated categorization technique has gained prominence, enabling efficient CVDs classification. The major focus of this work is to utilize deep learning (DL) approach for the identification of CVDs using ECG signals. The presented work incorporates two hybrid models: one-dimensional convolutional neural network (1D-CNN) with Recurrent Hopfield Neural Network (1DCNN-RHNN) and with Residual Network (1D-CNN-ResNet), to obtain important features from raw data and categorize them into different groups that correlate to CVD situation. The 1D-CNN-RHNN model achieved classification accuracy of 96.62% in the 4-class classification of normal, coronary artery disease (CAD), myocardial infarction (MI), and congestive heart failure (CHF) and the 1DCNN-ResNet model achieved classification accuracy of 95.75% in the 5-class classification of normal, CAD, MI, CHF and cardiomyopathy. The proposed model's functionality is validated with medical ECG data, and its outcomes are evaluated using various measures. Experimental findings demonstrate that the proposed models outperform other existing approaches in categorizing multiple classes. Our suggested approach might potentially help doctors screen for CVDs using ECG signals and is capable of being verified with larger databases.

Enhancing arrhythmia prediction through an adaptive deep reinforcement learning framework for ECG signal analysis

Heart diseases, particularly arrhythmia, remain a leading global cause of mortality. Electrocardiography (ECG) serves as a vital tool for monitoring and mitigating arrhythmia risks. Continuous proactive monitoring entails processing ECG time series data, offering critical insights to prevent severe outcomes like heart attacks and strokes. While deep learning models are widely employed for large-scale ECG data analysis, their effectiveness requires intricate hyperparameter tuning and architecture adjustments. This study presents an innovative framework incorporating a reinforcement learning-based approach to automatically fine-tune hyperparameters for a Convolutional Neural Network (CNN) model, addressing resource consumption and complexity issues. Our proposed model optimizes hyperparameters and network configurations by maximizing a reward function, overcoming challenges associated with resource-intensive and complex deep learning models. To assess its efficacy, we compare our automated fine-tuning model against both a non-optimized baseline model and a manually fine-tuned model. The findings demonstrate that our model outperforms the alternatives, achieving a higher accuracy of 97.4 %, reduced execution time (0.33 min), and lower mean square error (0.0081). Moreover, a comprehensive evaluation of resource consumption illustrates the lightweight nature of our proposed solution, minimizing overhead and complexity. Our model consistently converges to an optimal configuration, establishing its efficiency in predicting arrhythmia. In conclusion, our reinforcement learning-based hyperparameter tuning approach presents a lightweight and efficient solution for optimizing CNN models in ECG analysis. The results underscore its superiority over non-optimized and manually fine-tuned models, positioning it as a promising method for enhancing arrhythmia prediction accuracy while minimizing resource consumption and complexity.

An interpretable electrocardiogram-based model for predicting arrhythmia and ischemia in cardiovascular disease

IntroductionCardiovascular disease (CVD) is a leading cause of death and disability globally, with ischemia and arrhythmias being critical contributors. Ischemia, due to reduced myocardial blood flow, can lead to sudden cardiac death, while arrhythmias, marked by abnormal heart rhythms, are common in the elderly. Electrocardiography (ECG) is essential for the diagnosis of these conditions. This study aims to develop a clinically interpretable diagnostic framework for ischemia and arrhythmias using key ECG fiducial features. MethodsTo develop a robust ECG-based model for predicting cardiovascular diseases, we integrated data from three well-established ECG datasets: MIT-BIH Arrhythmia, European ST-T, and Fantasia. This aggregated dataset was employed to train multiple machine learning (ML) models aimed at automatically classifying heart conditions, including arrhythmia, ischemia, and healthy states. We designed a predictive framework utilizing boosting ML algorithms, enhanced by explainable artificial intelligence (XAI) techniques, to ensure high predictive performance in model interpretation. ResultsThe histogram gradient boosting classifier demonstrated superior classification performance, achieving an overall accuracy of 90 % in predicting heart disease based on ECG fiducial features. The model achieved area under the curve (AUC) scores of 0.99, 0.99, and 0.89 for the healthy, ischemic, and arrhythmic classes, respectively. XAI methods revealed that ECG fiducial features, such as the P-H, R-H, RR interval, QRS duration, QT interval, and ST segment, were significant diagnostic indicators for heart disease. ConclusionsThis study uses machine learning and XAI models to classify arrhythmia and ischemia from ECG data, enhancing interpretability clinical diagnostics for prevention and intervention to reduce disabilities.

Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram.

Background: In this study, we explored the potential of predicting dysglycemia in patients who need to continuously manage blood glucose levels using a non-invasive method via electrocardiography (ECG). Methods: The data were collected from patients with diabetes, and heart rate variability (HRV) features were extracted via ECG processing. A residual block-based one-dimensional convolution neural network model was used to predict dysglycemia. Results: The dysglycemia prediction results at each time point, including at the time of blood glucose measurement, 15 min prior to measurement, and 30 min prior to measurement, exhibited no significant differences compared with the blood glucose measurement values. This result confirmed that the proposed artificial intelligence model for dysglycemia prediction performed well at each time point. Additionally, to determine the optimal number of features required for predicting dysglycemia, 77 HRV features were individually eliminated in the order of decreasing importance with respect to the prediction accuracy; the optimal number of features for the model to predict dysglycemia was determined to be 12. The dysglycemia prediction results obtained 30 min prior to measurement, which exhibited the highest prediction range in this study, were as follows: accuracy = 90.5, sensitivity = 87.52, specificity = 92.74, and precision = 89.86. Conclusions: Furthermore, we determined that no significant differences exist in the blood glucose prediction results reported in previous studies, wherein various vital signs and blood glucose values were used as model inputs, and the results obtained in this study, wherein only ECG data were used to predict dysglycemia.

Long-term prognosis of patients with an SCN5A loss-of-function variant and progressive cardiac conduction disorder or Brugada syndrome

BackgroundThe long-term prognosis of patients with a loss-of-function variant in the cardiac sodium channel gene SCN5A is unknown. ObjectiveThis study aimed to evaluate the long-term arrhythmic risk in patients with an SCN5A loss-of-function variant to identify predictors of arrhythmic events. MethodsProbands and family members with (likely) pathogenic SCN5A loss-of-function variants were retrospectively included. Clinical and electrocardiographic data at baseline and last follow-up were collected. Patients with a history of cardiac arrest, sustained ventricular tachycardia, symptomatic or documented atrial tachy- or bradyarrhythmia, or arrhythmogenic syncope were categorized as symptomatic. Arrhythmic events at follow-up were defined as sudden death, aborted cardiac arrest, documented ventricular fibrillation, and/or sustained ventricular tachycardia. ResultsWe included 615 patients (349 men, 242 probands, 157 with a spontaneous type 1 Brugada electrocardiogram, and 111 symptomatic at baseline). During a median follow-up of 9.5 (Q1,Q3 5.0–14.3) years, arrhythmic events occurred in 41 patients (6.7%), equating an overall event rate of 0.7%/y: 2.0%/y in symptomatic and 0.3%/y in asymptomatic patients. In the overall study population, symptoms at baseline, male sex, and QRS prolongation were identified as independent predictors of arrhythmic events. In asymptomatic patients, male sex and QRS prolongation were also identified as predictors. Asymptomatic women with QRS interval < 100 ms did not experience arrhythmic events at follow-up. ConclusionKey predictors of arrhythmic risk in patients with an SCN5A loss-of-function variant, regardless of a Brugada syndrome diagnosis, are symptoms at baseline, male sex, and prolonged QRS interval. Our findings may enable more tailored management strategies in patients with an SCN5A loss-of-function variant based on their individual risk profiles.

Induction Chemotherapy-Related Covert Cardiac Remodeling in Pre-Autologous Hematopoietic Stem Cell Transplantation for Multiple Myeloma: A Retrospective Observational Study.

Autologous hematopoietic stem cell transplantation (ASCT) has emerged as a cornerstone in multiple myeloma (MM) management, offering the prospect of prolonged disease control. However, the induction chemotherapy drugs required prior to ASCT carry cardiovascular toxicity (CVT), potentially leading to a range of cardiovascular complications. This retrospective observational study, conducted at Guangdong Provincial People's Hospital from January 2020 to December 2023, analyzed 47 of the initial 173 patients who met the criteria. The cohort, comprising 22 males (46.81%) and 25 females (53.19%), had a mean age of 55.68 ± 11.38 years. They underwent various induction chemotherapy regimens, receiving a median of 5 (4-6) cycles of the course over an average duration of 7.10 ± 2.46 months. Before ASCT treatment following induction chemotherapy, echocardiographic findings indicated reductions in left ventricular end-systolic dimension, right atrial diameter, E-wave velocity, E/e' ratio, and the E/A ratio. The latter altered from a pretreatment value greater than 1 to posttreatment less than 1, marking diastolic dysfunction emergence or aggravation in 51.06% of patients. The electrocardiographic data indicate a reduced heart rate and prolonged P-wave duration and P-R duration, with an increase in arrhythmia incidence to 19.15% following induction chemotherapy. Induction chemotherapy, administered prior to ASCT in patients with MM, can lead to the emergence or aggravation of cardiac diastolic dysfunction and increase the incidence of arrhythmic events. Therefore, it is crucial to emphasize the importance of balancing the benefits and risks of induction chemotherapy to maximize its efficacy while minimizing CVT.

Twin atrioventricular nodes and accessory pathways in congenital heart diseases with abnormal atrioventricular connections: Association with the developmental hierarchy of cardiac morphology

BackgroundTwin atrioventricular (AV) nodes (TWAVNs) are common in heterotaxy syndrome. ObjectiveThe purpose of this study was to investigate the presence and implications of TWAVNs and accessory pathways in congenital heart diseases (CHDs) with abnormal AV connections. MethodsA retrospective study of a 1980–2022 cohort with sufficient electrocardiographic (ECG) data for review was conducted. ResultsWe enrolled 136 patients with heterotaxy syndrome, 70 with congenitally corrected transposition of the great arteries (ccTGA) (4 with Ebstein anomaly), 47 with double-inlet ventricle (DIV), and 63 with isolated AV canal defect. TWAVNs, detected in the same ECG (18%), in separate ECGs (70.8%), or after electrophysiological study (11.2%), were present in 43.4% of heterotaxy, 10.6% of DIV, 10% of ccTGA, and 1.6% of AV canal defect cases. Accessory pathways were noted in 11.4% of ccTGA and 4.4% of heterotaxy cases, but none of the DIV and AV canal cases. Actuarial incidence of supraventricular tachycardia (SVT) by age 10 was 0.354, 0.121, 0.022, and 0 in heterotaxy, ccTGA, DIV, and AV canal, respectively. In patients with TWAVNs, the risk of SVT varies according to the rate of TWAVNs in each CHD type, with risks of 65.5%, 58.3%, and 0% for TWAVNs rates >50%, 10%–50%, and <10%, respectively. Onset age of tachycardia did not differ between those with TWAVNs and those with accessory pathways. ConclusionIn CHD with abnormal AV connections, evidence suggested a developmental hierarchy in the propensity to exhibit TWAVNs, but not for accessory pathways. The earlier cardiac developmental errors occur, the higher the likelihood of TWAVNs and the greater the chance of SVT.

Use of ST/HR hysteresis decreases false positive rate in exercise electrocardiography test of middle-aged asymptomatic women

BackgroundExercise electrocardiography (ECG) is a common diagnostic and prognostic method for the detection of coronary artery disease (CAD). However, its accuracy in asymptomatic women has not been comprehensively investigated and the diagnostic criteria may require refinements. This study evaluated the performance of ECG-derived ST/HR-index, ST/HR hysteresis and ST-segment depression parameters among asymptomatic middle-aged women. Methods108 women (mean age 56 ± 4 years) performed exercise ECG test on treadmill until exhaustion three times within a nine-month period. False-positive rates of maximum ST/HR-index, ST/HR hysteresis, and ST-segment depression values measured from 12 leads at peak exercise and after one-minute recovery were evaluated with recommended diagnostic partition values. Repeatability was assessed with intraclass correlation (ICC) and Bland-Altman plot analysis. ResultsFalse-positive rate was lower for all variables when a two‑lead configuration was used instead of a single‑lead configuration. Using a two‑lead configuration, ST/HR hysteresis (0–1.9 %) and ST-segment depression after one-minute recovery (0–2.8 %) had lower false-positive rates compared to ST/HR index (3.7–20.4 %) and ST-segment depression at peak exercise (9.3–27.8 %). ICC values indicated moderate repeatability for ST/HR hysteresis while ST/HR index, ST-segment at peak exercise, and ST-segment after one-minute recovery had moderate-to-good repeatability. Bland-Altman analysis indicated poor repeatability for all evaluated ECG variables. ConclusionIn asymptomatic middle-aged women, practitioners should prefer the use of ST/HR hysteresis and ST-segment after one-minute recovery over the conventional ST-segment depression at peak exercise or ST/HR index, and evaluate the ECG data from a two‑lead configuration instead of single‑lead.

Significant regression of left ventricular hypertrophy in patient with arterial hypertension during antihypertensive treatment with perindopril and atorvastatin

Left ventricular hypertrophy (LVH) in arterial hypertension (AH) is one of the modifiable risk factors for cardiovascular events and serves as a justified goal for antihypertensive therapy. The article presents follow-up of a 60-year-old female with AH, hyperlipidemia, and discovered of LVH on the data of electrocardiography and echocardiography. In addition to the SokolowLyon and Cornell voltage criteria, the baseline echocardiographic examination revealed and increase in thickness of the ventricular septum and the left ventricular (LV) posterior wall, an increase in LV myocardial mass and LV myocardial mass index. The patient was recommended to take a drug of the class of angiotensin converting enzyme inhibitors perindopril and atorvastatin. The patient regularly received therapy and periodically underwent repeated echocardiographic examinations. After 40 months follow-up there are a significant decrease in septal hypertrophy (from 1.22 to 0.76 cm, -37.7%), LV posterior wall (from 1.05 to 0.49, -53.3%), as well as LV myocardial mass (from 197.9 to 96 g, -51.5%) and LV myocardial mass index (from 111.18 to 59.11 g/ m2, -51.5%). Thus, during the entire period of follow-up, the LV mass decreased almost twice. There was a decrease in the size of the left atrium, the relative wall thickness, with the same parameters of LV volumes, characteristics of systolic and diastolic function. Among the favorable factors contributing to significant LVH regression, high adherence to treatment, the efficacy of angiotensin converting enzyme inhibitors and the possible antihypertrophic influence of atorvastatin are discussed.

Predicting the severity of valvular heart diseases using ai integrated with ECG and anthropometric data

Abstract Background AI has been used in studies to predict the severity of aortic stenosis (AS) from electrocardiography (ECG), but the application of AI to predict the severity of aortic regurgitation (AR), mitral regurgitation (MR) and tricuspid regurgitation (TR) has not been widely explored. This study aimed to determine whether deep learning can predict the severity of valvular heart disease (AS, AR, MR and TR) assessed by echocardiography using ECG data. Methods and Results We analyzed data from 31,787 adult patients who underwent ECG and echocardiography within three days of each other between 2014 and 2019 at two hospitals. Valvular heart diseases, including AS, AR, MR, and TR, were classified into categories; normal, mild, moderate, and severe; according to guidelines. ECGs, along with patient age, gender, height, and weight, were inputs to a LightGBM machine learning model. The model was trained on 80.6% of the data, with the remaining 19.4% used for validation. The sensitivity for predicting moderate or severe valvular heart disease was 85% for AS, 88% for AR, 85% for MR, and 85% for TR. The negative predictive values for AS, AR, MR, and TR were all 100%. Prediction accuracies for the severity of each disease were AS at 0.857, AR at 0.871, MR at 0.855, and TR at 0.890. Predictions based on AS diagnostic parameters; mean pressure gradient, valve area, and maximum blood flow velocity also showed favorable accuracies at 0.891, 0.821, and 0.877, respectively. Conclusion Integrating AI with standard clinical parameters, including ECG data, height, and weight, showed potential in accurately predicting the severity of various valvular heart diseases. Further researches across diverse populations and settings are necessary to validate the approach's effectiveness and explore its impact on patient care.

Larger QRS but better hemodynamic function after high output His bundle pacing in patients with heart failure and narrow QRS. A paradox or a new concept?

Abstract Background QRS duration is an important factor in determining Cardiac Resynchronisation Therapy (CRT) response and a useful surrogate for electromechanical dyssynchrony. However, we don’t have studies confirming this concept in conduction system pacing. Purpose We performed His Bundle Pacing (HBP) with high output (3.5V/1msec) in HF patients with dilated or ischemic cardiomyopathy and narrow QRS to evaluate hemodynamic benefit independent of the QRS duration or AV delay shortening. Methods 14 patients with dilated (7 patients) or ischemic (7 patients) cardiomyopathy (EF&amp;lt;35%) and narrow QRS (&amp;lt;120ms) were referred for implantation of a defibrillator. We implanted an ICD- HBP system, and we collected clinical, echocardiographic, and electrocardiographic data. Results We obtained HBP in all 14 patients successfully. PQ after implantation was shorter from 172±48 to 143±16 (P=0.048) so as to pace the ventricle. Baseline QRS duration increased after pacing from 110±15 to 123±16ms (P=0.008) as measured by ECG analyzer. Despite this increase in QRS duration basal mean EF increased from 30±4% to 46±10% (P&amp;lt;0.001) after a median follow-up of 17 months. Left ventricular end-diastolic diameter and left ventricular end-diastolic volume decreased from 62±5 to 57±5mm (P=0.013) and from 195±69 to 126±27ml (P&amp;lt;0.001), respectively. Left ventricular end-systolic volume decreased from 139±58 to 69±27 (P&amp;lt;0.001). TAPSE increased from 19±4 to 27±4. NYHA class decreased by one class in every patient after only one month. HBP threshold was 1.05V±0.7/1msec and raised to 1.33±0.93/1msec (due mainly to 2 patients with very high threshold). The impedance was 588±109 and decreased to 484±69 Ohms. R-wave at implant was 3.2±1.8mV. In 2 out of 14 patients, threshold increased but was still less than 3.5V/1msec. Conclusions HBP in HF patients with dilated or ischemic cardiomyopathy and narrow QRS improves hemodynamic function in right and left ventricle and decreases NYHA class. This benefit appears to be produced by a higher stimulation output despite a larger QRS after pacing. We exclude that a very short AV delay could be favorable, by measuring acute EF changes only after increasing the pacing output without AV delay modifications. To our knowledge these are the first cases of beneficial HBP in HF patients with narrow QRS. Interestingly, larger QRS corresponds to higher EF in patients with high output HIs Bundle Pacing.

Artificial intelligence for automated left ventricular systolic dysfunction detection using a wearable cardiac patch incorporated with synchronized phonocardiogram and electrocardiogram

Abstract Background Left ventricular systolic dysfunction (LVSD) is characterized by a reduced left ventricular ejection fraction (LVEF) and is associated with three times the risk of developing Heart failure (HF). We aimed to test an artificial intelligence (AI) algorithm applied to synchronized phonocardiography (PCG) and electrocardiography (ECG) signals, recorded with a wearable device, to validate its potential as a screening tool for LVSD. Methods Using ECG and PCG data collected from 1960 admitted patients in a hospital, we trained an AI algorithm to detect the LVSD. There are two ways of defining LVSD, one group is defined by LVEF &amp;lt;50% and the other is defined by LVEF 40% as measured by echocardiography. The AI algorithm followed a two-step detection structure. In the first phase, ECG signals were processed by wavelet denoising and baseline wander correction, and PCG signals were converted to the frequency domain via wavelet transformation. We developed a 1D CNN-based U-Net variant model to pinpoint electromechanical activation time (EMAT). Addressing the challenge of processing multimodal signals, we devised a contrastive learning approach that mandates the encoders for different modalities to generate embeddings within the same feature space. This trained encoder, along with the decoder in U-Net, is subsequently fine-tuned for EMAT detection. The AI-EMAT% was obtained by adjusting detected EMAT based on RR interval. For the second phase, random forest regression is used to estimate LVEF, incorporating features such as AI-EMAT% and clinical data, including age and gender. For LVSD detection, we evaluated the performance of methods by bifurcating AI-estimated LVEF% (AI-predict LVEF%) based on thresholds of 40% and 50%, and by directly estimating AI-EMAT% cutoff. The evaluation of LVSD detection algorithm, conducted via 2-fold cross-validation, involves dividing the dataset into two subsets, with each alternately serving as training and testing sets to ensure a fair assessment. Results We recruited 1960 patients (1430 [72.96%] male). 357 (18.21%) had an ejection fraction of 50% or lower, and 157 (8.01%) had an ejection fraction of 40% or lower. In detecting LVSD of LVEF&amp;lt;50%, AI-EMAT% yielded an AUROC of 0.86, sensitivity of 73.7%, and specificity of 80.7%. While AI-predict LVEF% approach resulted in an AUROC of 0.89, sensitivity of 81.8%, and specificity of 81.5%. In detecting LVSD of LVEF&amp;lt;40%, AI-EMAT% yielded an AUROC of 0.89, sensitivity of 85.4%, and specificity of 76.0%. While AI-predict LVEF% outputs resulted in an AUROC of 0.91, sensitivity of 92.4%, and specificity of 76.8%. Conclusions We developed an automated algorithm to detect LVSD using a wearable device that incorporated synchronized PCG and ECG signals, demonstrating robust performance across different subgroups. This approach represents a strategy for automated screening of LV systolic dysfunction, particularly beneficial in resource-limited settings.Baseline and AUROCAI algorithm

Prevalence of Brugada ECG pattern in psychiatric patients: a longitudinal prospective cohort of 550 patients

Abstract Background Brugada syndrome (BrS) is an inherited disorder with autosomal dominant transmission, which occurs predominantly in males in the third to fourth decade of life with resting syncope, nocturnal agonic breathing, major ventricular arrhythmias and sudden death. Arrhythmic complications can occur spontaneously or following exposure to triggers such as fever, or certain categories of medications, including psychiatric drugs. Aim of the study To evaluate the prevalence of Brugada ECG pattern in psychiatric patients. Methods from January 2023 to Decembre 2023, 550 consecutive patients who were admitted to the psychiatric department of our university hospital were enrolled. For each patient, electrocardiographic and anamnestic data including psychiatric diagnosis, current drug therapy and family history were collected. Results Twenty-one out of five-hundred and fifty patients (3.8%) presented with a Brugada ECG pattern. Mean age was 42±6 years and seventeen out of twenty-one patients (81%) were male. Two patients exhibited a Spontaneous type 1 Brugada ECG pattern meanwhile nineteen patients had a Brugada type 2 or 3 ECG pattern. When compared to the general population, those with Brugada ECG had longer P wave duration (110±7 vs 103±13 msec p&amp;lt;0.01) and longer QRS duration (97±14 vs 89±12 msec p=0.04). Evaluating psychiatric disorders, patients with Brugada ECG had high prevalence of mood disorders (10 out of 21, 47%). Other disorders were schizophrenia (3 out of 21, 14%), cognitive retardation (5 out of 21, 23%), and attention disorder (1 out of 21, 5%). Moreover 4 patients were drug addicted. Only 5 out of 21 (24%) patients had a family history of psychiatric and neurological disorders. Conclusions Brugada ECG pattern has a prevalence of 3.8% in a large cohort of psychiatric patients. Mood disorders was the most common diagnosis in this cohort. Large prospective registries are needed to evaluate the clinical feature and potential clinical implication of this findings.

The impact of QRS complex fragmentation status before and after cardiac resynchronization therapy with defibrillator implantation: Is it a predictor of arrhythmic events?

Abstract Introduction Cardiac resynchronization therapy with an implantable cardioverter defibrillator (CRT-D) has demonstrated efficacy in reducing mortality, hospitalization for heart failure (HF), and sudden cardiac death among subgroups of patients with HF with reduced ejection fraction (HFrEF). However, this population is highly heterogeneous, necessitating risk stratification. Several clinical findings, including QRS complex fragmentation (QRSf), have been identified as potential prognostic factors for arrhythmic risk. Nonetheless, evidence supporting the prognostic significance of QRSf in patients undergoing CRT-D remains limited. Purpose The aim of this study is to assess whether alterations in QRSf status, before and after CRT-D implantation, can predict arrhythmic events (AE) in patients with HFrEF. Methods This study included consecutive patients underwent CRT-D implantation at our center from October 2009 to December 2022. Demographic, clinical, electrocardiographic and echocardiographic data were obtained from electronic medical records and telephone interviews. QRSf was defined by the presence of various RSR′ patterns, including additional R waves (R′) or notching of the R and S wave in two contiguous leads corresponding to a primary coronary artery territory on a 12-lead electrocardiogram conducted before and after CRT-D implantation. An AE was characterized by appropriate ICD therapy (antitachycardia pacing or shock) for a ventricular arrhythmia or sustained ventricular arrhythmia without the need for ICD therapy. Results A total of 176 patients were included (mean age 69.9 ± 9.6 years with 80.6% male). Ischemic etiology accounted for 54.6% of cases of LV systolic dysfunction, with a mean baseline LVEF of 23.6%. CRT-D was indicated as secondary prevention in 18 patients. At baseline, 114 patients (64.8%) presented QRSf. Following CRT implantation, 17 patients (9.8%) without previous QRSf developed QRSf, while 26 patients (14.9%) resolved QRSf. The median follow-up was 43.6 months (interquartile range 19.5-81.1). During follow-up, 90 patients died and 29 AE occurred (16.5%). Among these, 23 patients had QRSf before and after CRT, whereas 6 AE occurred in patients without QRSf. Multivariate survival analysis using Cox regression demonstrated that the presence of QRSf before and after CRT-D was an independent predictor of AE (HR 3.56; 95% CI 1.00-12.70). Univariate and multivariate Cox regression analyses are presented in Table 1. Kaplan-Meier curves for AE in relation to QRSf status before and after CRT-D are illustrated in Figure 1. Conclusions Our findings suggest that the presence of QRSf and its persistence following CRT-D implantation appears to be an independent predictor of AE after CRT-D implantation in patients with HFrEF. Therefore, these patients would benefit from closer monitoring as they exhibit a heightened risk of arrhythmic events. Nevertheless, further research is warranted to validate these results.Table 1.Cox Regression Analysis.

Safety, feasibility, and acceptability of a novel device to monitor ischaemic stroke patients

This study assessed the safety, feasibility, and acceptability of a novel device to monitor ischaemic stroke patients. The device captured electroencephalography (EEG) and electrocardiography (ECG) data to compute an ECG-based metric, termed the Electrocardiography Brain Perfusion index (EBPi), which may function as a proxy for cerebral blood flow (CBF). Seventeen ischaemic stroke patients wore the device for nine hours and reported feedback at 1, 3, 6 and 9 h regarding user experience, comfort, and satisfaction (acceptability). Safety was assessed as the number of adverse events reported. Feasibility was assessed as the percentage of uninterrupted EEG/ECG data recorded (data capture efficiency). No adverse events were reported, only minor incidences of discomfort. Overall device comfort (mean ± 1 standard deviation (SD) (range)) (92.5% ± 10.3% (57.0–100%)) and data capture efficiency (mean ± 1 SD (range)) (95.8% ± 6.8% (54.8–100%)) were very high with relatively low variance. The device didn’t restrict participants from receiving clinical care and rarely (n = 6) restricted participants from undertaking routine tasks. This study provides a promising evidence base for the deployment of the device in a clinical setting. If clinically validated, EBPi may be able to detect CBF changes to monitor early neurological deterioration and treatment outcomes, thus filling an important gap in current monitoring options. TRIAL REGISTRATION: The study was prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12622000112763).

Prediction of left ventricular thrombus after myocardial infarction: a cardiac magnetic resonance-based prospective registry

BackgroundLeft ventricular thrombus (LVTh) is a severe complication after ST-segment elevation myocardial infarction (STEMI). ObjectivesWe aim to predict LVTh occurrence by cardiac magnetic resonance (CMR) using clinical, echocardiographic, and electrocardiographic (ECG) variables readily available at admission. MethodsWe included 590 reperfused STEMI patients who underwent early (1-week) and/or late (6-month) CMR in our institution. Baseline clinical, echocardiographic (left ventricular ejection fraction -LVEF-) and ECG data (summatory of ST-segment elevation -sum-STE- and Q-wave and residual ST-elevation >1 mm -Q-STE-) during admission were registered. Multivariate binary logistic regression models and receiver operating characteristic curves were computed for LVTh prediction. ResultsLVTh was detected by CMR in 43 (7.3 %) patients and was predicted by previous chronic coronary syndrome (CCS, HR 4.74 [1.82–12.35], p = 0.001), anterior STEMI (HR 10.93 [2.47–48.31], p = 0.002), LVEF (HR 0.96 [0.93–0.99] per %, p = 0.008), maximum sum-STE (HR 1.04 [1.01–1.07] per mm, p = 0.04), and Q-STE (HR 1.31 [1.08–1.6] per lead, p = 0.008). High-risk patients with both major (anterior STEMI and Q-STE in ≥1 leads) and 1–3 minor (CCS, maximum sum-STE >10 mm, LVEF <50%) factors showed the highest LVTh risk (19.6 % within 6 months). The model showed excellent discrimination ability (area under the curve=0.85 [0.81–0.9], p < 0.001). Simplified 4-variable (excluding sum-STE) and 3-variable (also excluding CCS) risk scores showed similar discrimination ability and were externally validated. ConclusionsLVTh within 6 months post-STEMI can be predicted using pre-discharge clinical (anterior infarction and CCS), echocardiographic (LVEF), and ECG (sum-STE and Q-STE) data. Our results can help select patients who should undergo CMR after STEMI for LVTh detection.

Electrocardiographic Parameters in Iranian Children Referring to Hospitals Affiliated with Mashhad University of Medical Sciences

Abstract Background: Various studies and guidelines have been published for the interpretation of electrocardiograms (ECGs) around the world. However, there is insufficient evidence supporting the relationship between race, ethnicity, and ECG parameters in the pediatric population. Objectives: This study is a pioneering effort to create a standardized registry in Iran, aiming to determine normal electrocardiographic parameters for Iranian children. Methodology: This preliminary pilot study was a descriptive cross-sectional type. Digital electrocardiography data (with 12 leads) of 382 boys and girls aged 1–216 months (18 years) who visited 3 children’s hospitals affiliated with Mashhad University of Medical Sciences were collected over 2 years. The main ECG values examined in this study included the P, QRS, T, U waves, PR interval, QT interval, QTc interval, heart rate (HR), and PR segment. For describing quantitative data, measures of central tendency such as mean and median, and measures of dispersion including standard deviation, range, and quartiles were used. For describing qualitative variables, the percentage of frequency was used. Quantitative data descriptions were done separately by gender and age groups. Results: HR and ECG parameters by sex and age group (1 month–18 years) were measured in this study. Conclusion: Our findings differ from those of previous studies. These differences might be due to the limitations of this study, such as the sample size and the diversity of racial backgrounds. To fully comprehend these discrepancies, it is important to establish registries and conduct studies with larger sample sizes.

Supervised machine learning on ECG features to classify sleep in non-critically ill children.

Despite frequent sleep disruption in the paediatric intensive care unit (PICU), bedside sleep monitoring in real-time is currently not available. Supervised machine learning (ML) applied to electrocardiography (ECG) data may provide a solution, since cardiovascular dynamics are directly modulated by the autonomic nervous system (ANS) during sleep. Retrospective study using hospital-based polysomnography (PSG) recordings obtained in non-critically ill children between 2017 and 2021. Six age categories were defined: 6-12 months, 1-3 years, 3-5 years, 5-9 years, 9-13 years and 13-18 years. Features were derived in time, frequency and non-linear domain from pre-processed ECG data. Sleep classification models were developed for two, three, four and five state using logistic regression (LR), random forest (RF) and XGBoost (XGB) classifiers during five-fold nested cross-validation. Models were additionally validated across age categories. A total of 90 non-critically ill children were included with a median (Q1, Q3) recording length of 549.0 (494.8, 601.3) minutes. The three models obtained AUROC 0.72 - 0.78 with minimal variation across classifiers and age categories. Balanced accuracies were 0.70 - 0.72, 0.59 - 0.61, 0.50 - 0.51 and 0.41 - 0.42 for two, three, four and five state. Generally, the XGB model obtained the highest balanced accuracy (p < 0.05), except for five state where LR excelled (p = 0.67). ECG-based ML models are a promising and non-invasive method for automated sleep classification directly at the bedside of non-critically ill children aged 6 months to 18 years. Models obtained moderate-to-good performance for two and three state classification.

Cardiovascular Effects of Treatment with Atropine 0.01% Eyedrops to prevent the Progression of Childhood Myopia

The effectiveness of atropine eyedrops in preventing myopia progression in children has been confirmed, but cardiovascular effects have never been analysed. The objective of this study was to assess cardiovascular changes after treatment with atropine 0.01% eyedrops in 60 paediatric patients who were treated with atropine eyedrops due to myopic progression. Patients were analysed before treatment and 3 months after starting 1 drop daily. The parameters assessed were cardiovascular constants and electrocardiographic data. The average age was 10.2 years, with a higher women percentage. The average follow-up period was 3.5 months. No patients presented pathological electrocardiographic findings and one patient reported symptoms: palpitations. A statistically significant decrease in heart rate (79 vs. 75 bpm) was reported, but without clinical significance. No changes in blood pressure were observed. In conclusion, chronic therapy with low doses of atropine as a treatment for myopia progression does not result in pathological heart changes.